Rationale and Objectives
Diffusion magnetic resonance imaging may be useful in tracking tumor growth and response to treatment. However, studies using these measures may lack statistical power to draw definitive conclusions regarding changes in tumor cellularity. Using apparent diffusion coefficient values taken from the literature, the investigators estimated sample sizes for a range of changes to the mean.
Materials and Methods
A literature search was performed of studies measuring the average apparent diffusion coefficients for various bodily tissues, and the mean and standard deviation from each study were recorded. Analyses of statistical power were then performed using these values and comparing them to a population of healthy controls.
Results
Tumor cellularity as measured by apparent diffusion coefficients may have high sensitivity, but the analyses indicate that investigations in this field may potentially suffer from low statistical power. For example, the findings indicate that samples of <20 patients may require a mean change of approximately 25% between study conditions.
Conclusions
Suggestions are offered for improvements in methodologic approaches and in data reporting to assist in overcoming the limitations of small sample sizes. On the basis of this literature review, reference values are provided to help investigators estimate study sample size to achieve adequate statistical power.
Advances in magnetic resonance imaging (MRI) have provided new means of tracking tumor progression and response to treatment. In particular, diffusion-weighted imaging, which tracks the microscopic rate of water diffusion within tissues, may hold many advantages over traditional anatomic MRI techniques by providing information about tissue cellularity and the integrity of cell membranes . Recent research has focused on the apparent diffusion coefficient (ADC), which is reduced as cell concentration increases and is more sensitive to changes in tumor progression or response to treatment than traditional measures, such as tumor volume . ADC values may potentially predict tumor grade and response to therapy, possibly because of the inherent barrier to water diffusion of tumor cells . Numerous studies have found large changes in ADCs in response to chemoradiation , stereotactic irradiation , convection-enhanced delivery of therapeutic agents , and other therapies . Generally, increases in ADC values after therapy are associated with a positive treatment response, which may be due to decreased cell proliferation and density and an increased extracellular space from cell shrinkage during apoptotic cell processes seen in a positive therapeutic response . In addition to changes in ADCs over time and among patient groups, it has also been noted that intratumoral ADC may be valuable, because areas of tumor in which the ADC is stable may represent persistent disease . Additionally, minimum intratumoral ADC values have been shown to have clinical relevance, as studies on human malignant gliomas have shown a correlation between minimum ADC values with higher tumor grade and shorter survival times .
Given these findings, ADC and other diffusion parameters may have a significant role in noninvasively quantifying response to treatment, guiding treatment plans, and research. However, measurements of ADC are highly variable across patients, and many studies are underpowered to detect small or moderate changes in ADC during the course of therapy. This may lead to the selective publication of research that identifies large effects of interventions on ADCs, while subtler ADC changes may be lost as false-negatives. Challenges facing the application of diffusion-weighted imaging have only recently begun to be explored. For example, Heusner et al found diffusion imaging to be a sensitive, though relatively nonspecific, diagnostic tool in identifying metastatic breast tumors. Considering work in rodents that has demonstrated the high sensitivity of ADC to subcellular changes in tumor pathology , it is probable that small changes in ADC are indeed present but have been missed by studies with low power.
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Materials and methods
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n=2(z1−α/2+z1−β)2(μ0−μ1σ)2 n
=
2
(
z
1
−
α
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2
+
z
1
−
β
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2
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An additional analysis was performed using mean and SD values for ADC gathered by an independent meta-analysis for head and neck cancer reported by Vandecaveye et al . In this analysis of the literature, the mean and SD of ADC were calculated to be 0.99 × 10 −3 and 0.26 × 10 −3 mm 2 /s, respectively, for lymph nodes that were negative on biopsy.
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Results
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Discussion
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Supplementary Data
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Supplementary Table 1
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References
1. Hamstra D.A., Rehemtulla A., Ross B.D.: Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol 2007; 25: pp. 4104-4109.
2. Chenevert T.L., Stegman L.D., Taylor J.M., et. al.: Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 2000; 92: pp. 2029-2036.
3. Humphries P.D., Sebire N.J., Siegel M.J., et. al.: Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology 2007; 245: pp. 848-854.
4. Chenevert T.L., McKeever P.E., Ross B.D.: Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 1997; 3: pp. 1457-1466.
5. Provenzale J.M., Mukundan S., Barboriak D.P.: Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 2006; 239: pp. 632-649.
6. Kremser C., Judmaier W., Hein P., et. al.: Preliminary results on the influence of chemoradiation on apparent diffusion coefficients of primary rectal carcinoma measured by magnetic resonance imaging. Strahlenther Onkol 2003; 179: pp. 641-649.
7. Tomura N., Narita K., Izumi J., et. al.: Diffusion changes in a tumor and peritumoral tissue after stereotactic irradiation for brain tumors: possible prediction of treatment response. J Comput Assist Tomogr 2006; 30: pp. 496-500.
8. Mardor Y., Roth Y., Lidar Z., et. al.: Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 2001; 61: pp. 4971-4973.
9. Moffat B.A., Chenevert T.L., Lawrence T.S., et. al.: Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A 2005; 102: pp. 5524-5529.
10. Hamstra D.A., Chenevert T.L., Moffat B.A., et. al.: Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 2005; 102: pp. 16759-16764.
11. Higano S., Yun X., Kumabe T., et. al.: Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 2006; 241: pp. 839-846.
12. Heusner T., Kuemmel S., Koeninger A., et. al.: Diagnostic value of diffusion-weighted magnetic resonance imaging (DWI) compared to FDG PET/CT for whole-body breast cancer staging. Eur J Nucl Med Mol Imaging 2010; 37: pp. 1077-1086.
13. Larsen R.J., Marx M.L.: An introduction to mathematical statistics and its applications.3rd ed.2001.Prentice HallUpper Saddle River, NJ
14. Fitzner K., Heckinger E.: Sample size calculation and power analysis: a quick review. Diabetes Educ 2010; 36: pp. 701-707.
15. Phan T.G., Donnan G.A., Davis S.M., et. al., MR Stroke Collaborative Group: Proof-of-principle phase II MRI studies in stroke: sample size estimates from dichotomous and continuous data. Stroke 2006; 37: pp. 2521-2525.
16. Hansen W.B., Collins L.M.: Seven ways to increase power without increasing N. NIDA Res Monogr 1994; 142: pp. 184-195.
17. Partridge S.C., Ziadloo A., Murthy R., et. al.: Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J Magn Reson Imaging 2010; 31: pp. 339-347.
18. Vandecaveye V., de Keyzer F., Dirix P., et. al.: Applications of diffusion-weighted magnetic resonance imaging in head and neck squamous cell carcinoma. Neuroradiology 2010; 52: pp. 773-784.
19. Mori S., Oishi K., Jiang H., et. al.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 2008; 40: pp. 570-582.
20. Smith S.M., Jenkinson M., Woolrich M.W., et. al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23: pp. S208-S219.